The paper presents a novel idea of proposing the application of Variational Autoencoders (VAEs) in crime detection for predicting face aging and deaging, which is one of the potential challenge of forensic science. VA...
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The rapid expansion of the Internet of Things (IoT) has led to its widespread adoption across various domains, including smart cities, industry, and agriculture. IoT systems consist of billions of interconnected devic...
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Accurately predicting pharmacokinetic (PK) parameters such as absorption, distribution, metabolism, and excretion (ADME) is essential for optimizing drug efficacy, safety, and development timelines. Traditional experi...
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Intrusion detection is a prominent factor in the cybersecurity domain that prevents the network from malicious attacks. Cloud security is not satisfactory for securing the user’s information because it is based on st...
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In this study, the cloud computing platform is equipped with a hybrid multi-objective meta-heuristic optimization-based load balancing model. Physical Machine (PM) allocates a specific virtual machine (VM) depending o...
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In this study, the cloud computing platform is equipped with a hybrid multi-objective meta-heuristic optimization-based load balancing model. Physical Machine (PM) allocates a specific virtual machine (VM) depending on multiple criteria, such as the amount of memory used, migration expenses, power usage, and the load balancing settings, upon receiving a request to handle a cloud user's duties (‘Response time, Turnaround time, and Server load’). Additionally, the optimal virtual machine (VM) is chosen for efficient load balancing by utilizing the recently proposed hybrid optimization approach. The Cat and Mouse-Based Optimizer (CMBO) and Standard Dingo Optimizer (DXO) are conceptually blended together to get the proposed hybridization method known as Dingo Customized Cat mouse Optimization (DCCO). The developed method achieves the lowest server load in cloud environment 1 is 33.3%, 40%, 42.3%, 40.2%, 36.8%, 42.5%, 50%, 40.2%, 39.2% improved over MOA, ABC, CSO, SSO, SSA, ACSO, SMO, CMBO, BOA, DOX, and FF-PSO, respectively. Finally, the projected DCCO model has been evaluated in terms of makespan, memory usage, migration cost, response time, usage of power server load, turnaround time, throughput, and convergence. ABBREVIATION: CDC, cloud data center;CMODLB, Clustering-based Multiple Objective Dynamic Load Balancing As A Load Balancing;CSP, Cloud service providers;CSSA, Chaotic Squirrel Search Algorithm;DA, Dragonfly Algorithm;ED, Euclidean Distance;EDA-GA, Estimation Of Distribution Algorithm And GA;FF, FireFly algorithm;GA, Genetic Algorithm;HHO, Harris Hawk Optimization;IaaS, Infrastructure-as-a-Service;MGWO, Modified Mean Grey Wolf Optimization Algorithm;MMHHO, Mantaray modified multi-objective Harris Hawk optimization;MRFO, Manta Ray Forging Optimization;PaaS, Platform-as-a-Service;PM, Physical Machine;PSO, Particle Swarm Optimization;SaaS, Software-as-a-Service;SAW, Sample additive weighting;SLA-LB, Service Level Agreement-Based Load Balancing;TBTS, Threshold-Bas
Researchers and scientists need rapid access to text documents such as research papers,source code and *** research documents are available on the Internet and need more time to retrieve exact documents based on *** e...
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Researchers and scientists need rapid access to text documents such as research papers,source code and *** research documents are available on the Internet and need more time to retrieve exact documents based on *** efficient classification algorithm for retrieving documents based on keyword words is *** traditional algorithm performs less because it never considers words’polysemy and the relationship between bag-of-words in *** solve the above problem,Semantic Featured Convolution Neural Networks(SF-CNN)is proposed to obtain the key relationships among the searching keywords and build a structure for matching the words for retrieving correct text *** proposed SF-CNN is based on deep semantic-based bag-of-word representation for document *** deep learning methods such as Convolutional Neural Network and Recurrent Neural Network never use semantic representation for *** experiment is performed with different document datasets for evaluating the performance of the proposed SF-CNN ***-CNN classifies the documents with an accuracy of 94%than the traditional algorithms.
Combining auto encoders and hybrid cellular automata provides a novel way to identify anomalies in structured data in the field of anomaly detection. Dimensionality reduction and extracting the features is one of the ...
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Recent advances in Generative AI, have encouraged many industries to use generative models in their products for generating components such as images, text, audio, or video. Generative foundation models like Large Lan...
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Vehicular Ad-Hoc Networks (VANETs) are studied wireless networks that enable communication among vehicles and roadside infrastructure. The role a vital play in improving on-road safety, efficacy, and convenience by en...
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Brain tumor detection and division is a difficult tedious undertaking in clinical image *** it comes to the new technology that enables accurate identification of the mysterious tissues of the brain,magnetic resonance...
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Brain tumor detection and division is a difficult tedious undertaking in clinical image *** it comes to the new technology that enables accurate identification of the mysterious tissues of the brain,magnetic resonance imaging(MRI)is a great *** is possible to alter the tumor’s size and shape at any time for any number of patients by using the Brain *** have a difficult time sorting and classifying tumors from multiple *** tumors may be accurately detected using a new approach called Nonlinear Teager-Kaiser Iterative Infomax Boost Clustering-Based Image Segmentation(NTKFIBC-IS).Teager-Kaiser filtering is used to reduce noise artifacts and improve the quality of images before they are *** clinical characteristics are then retrieved and analyzed statistically to identify brain *** use of a BraTS2015 database enables the proposed approach to be used for both qualitative and quantitative *** dataset was used to do experimental evaluations on several metrics such as peak signal-to-noise ratios,illness detection accuracy,and false-positive rates as well as disease detection time as a function of a picture *** segmentation delivers greater accuracy in detecting brain tumors with minimal time consumption and false-positive rates than current stateof-the-art approaches.
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